Detection and monitoring of sleep apnea conditions

ABSTRACT

A method of detecting sleep apnea includes generating a cardiac signal indicating activity of a heart of a patient. The method further includes determining a short-term average heart rate and a long-term average heart rate. The method further includes determining a start and end of a heart rate cycle based on the short-term average heart rate and the long-term average heart rate. The method further includes determining physiological parameter values occurring during the heart rate cycle. The method further includes determining whether patient has or has not experienced a sleep apnea event based on whether one or more conditions are satisfied by one or more parameter values for one or more heart rate cycles and responsively generating an indication that patient has or has not experienced a sleep apnea event.

This application is a continuation of U.S. application Ser. No.17/116,482, filed Dec. 9, 2020, the entire content of which isincorporated herein by reference.

TECHNICAL FIELD

This disclosure generally relates to techniques for monitoringphysiological conditions of a patient and, more particularly, totechniques for detecting a sleep apnea episode.

BACKGROUND

When functioning properly, a heart maintains its own intrinsic rhythm,and is capable of pumping adequate blood throughout a circulatorysystem. This intrinsic rhythm is a function of intrinsic signalsgenerated by the sinoatrial (SA) node located in the upper right atrium.The SA node periodically depolarizes, which in turn causes the atrialheart tissue to depolarize so that right and left atria contract as thedepolarization travels through the atrial heart tissue. The atrialdepolarization signal is also received by the atrioventricular node, orAV node, which, in turn, triggers a subsequent ventriculardepolarization signal that travels through and depolarizes theventricular heart tissue causing the right and left ventricles tocontract.

A condition known as sleep apnea can diminish cardiac output and posevarious risks to patients, particularly those who are susceptible toheart failure. Sleep apnea is a sleep disorder that involves thetemporary cessation of respiratory airflow during sleep. In variousscenarios, sleep apnea may be characterized by one or both of pauses inbreathing or periods of shallow breathing during sleep.

Sleep apnea is generally considered a medical syndrome that occurs invarious forms. One recognized form of sleep apnea is “central sleepapnea,” which is associated with a failure of the central nervous systemto automatically initiate and control respiration. Another recognizedform of sleep apnea is “obstructive sleep apnea,” which is associatedwith an obstruction of the airways due to airway collapse. Yet anotherrecognized form of sleep apnea is a mixed form that may include acentral nervous system failure to drive ventilatory effort combined withan obstructive apnea.

Possible effects of sleep apnea include daytime sleepiness, impairedalertness, and various associated cardiovascular diseases, which in turncan significantly impair patient lifestyle and increase morbidity risk.In some cases, obstructive sleep apnea can lead to death due to lack ofoxygen to vital organs of the body. Various approaches have been takento detect and treat sleep apnea.

SUMMARY

Some existing monitoring systems have relied on respiratory measurementdata to detect an episode of sleep apnea. For example, algorithms basedon cardiac signals have been used to detect sleep apnea episodes.However, some sleep apnea algorithms have a significant false positiveerror rate. Thus, there is a need for techniques for accurate detectionof sleep apnea episodes.

In one example, the disclosure provides a method of detecting sleepapnea. The method includes sensing, by sensing circuitry, a cardiacsignal indicating activity of a heart of a patient. The method furtherincludes determining, by processing circuitry and based on the cardiacsignal, a short-term average of a heart rate of the patient and along-term average of the heart rate of the patient, the short-termaverage of the heart rate of the patient being based on fewer heartbeats than the long-term average of the heart rate of the patient. Themethod further includes determining, by the processing circuitry, that aheart rate cycle has started based on a first time the short-termaverage of the heart rate of the patient changes from being less thanthe long-term average of the heart rate of the patient to being greaterthan the long-term average of the heart rate of the patient. The methodfurther includes determining, by the processing circuitry, that theheart rate cycle has ended based on a second time the short-term averageof the heart rate of the patient changes from being less than thelong-term average of the heart rate of the patient to being greater thanthe long-term average of the heart rate of the patient. The methodfurther includes determining, by the processing circuitry, apeak-to-valley time interval, the peak-to-valley time interval being atime interval between a maximum short-term average of the heart rateduring the heart rate cycle and a minimum short-term average of theheart rate during the heart rate cycle. The method further includesdetermining, by the processing circuitry, whether one or more conditionsof a plurality of conditions are satisfied for the heart rate cycle, theplurality of conditions comprising a peak-to-valley time intervalcondition that the peak-to-valley time interval is greater than a lowerpeak-to-valley time threshold and less than an upper peak-to-valley timethreshold. The method further includes determining, by the processingcircuitry, that the patient has experienced a sleep apnea episode basedat least in part on one or more conditions being satisfied for the heartrate cycle. The method further includes generating, by the processingcircuitry, an indication that the patient has experienced a sleep apneaepisode.

In another example, the disclosure provides a system for detecting sleepapnea. The system includes sensing circuitry and processing circuitry.The sensing circuitry is configured to sense a cardiac signal indicatingactivity of a heart of a patient. The processing circuitry is configuredto determine, based on the cardiac signal, a short-term average of aheart rate of the patient and a long-term average of the heart rate ofthe patient, the short-term average of the heart rate of the patientbeing based on fewer heart beats than the long-term average of the heartrate of the patient. The processing circuitry is further configured todetermine that a heart rate cycle has started based on a first time theshort-term average of the heart rate of the patient changes from beingless than the long-term average of the heart rate of the patient tobeing greater than the long-term average of the heart rate of thepatient. The processing circuitry is further configured to determinethat the heart rate cycle has ended based on a second time theshort-term average of the heart rate of the patient changes from beingless than the long-term average of the heart rate of the patient tobeing greater than the long-term average of the heart rate of thepatient. The processing circuitry is further configured to determine apeak-to-valley time interval, the peak-to-valley time interval being atime interval between a maximum short-term average of the heart rateduring the heart rate cycle and a minimum short-term average of theheart rate during the heart rate cycle. The processing circuitry isfurther configured to determine whether one or more conditions of aplurality of conditions are satisfied for the heart rate cycle, whereone condition of the plurality of conditions is that the peak-to-valleytime interval is greater than a lower peak-to-valley time threshold andless than an upper peak-to-valley time threshold. The processingcircuitry is further configured to determine that the patient hasexperienced a sleep apnea episode based at least in part on one or moreconditions being satisfied for the heart rate cycle. The processingcircuitry is further configured to generate an indication that thepatient has experienced a sleep apnea episode.

In another example, the disclosure provides a non-transitorycomputer-readable storage medium encoded with instructions. Theinstructions, when executed, cause processing circuitry of a medicaldevice system to receive a cardiac signal indicating activity of a heartof a patient. The instructions further cause the processing circuitry todetermine, based on the cardiac signal, a short-term average of a heartrate of the patient and a long-term average of the heart rate of thepatient, the short-term average of the heart rate of the patient beingbased on fewer heart beats than the long-term average of the heart rateof the patient. The instructions further cause the processing circuitryto determine that a heart rate cycle has started based on a first timethe short-term average of the heart rate of the patient changes frombeing less than the long-term average of the heart rate of the patientto being greater than the long-term average of the heart rate of thepatient. The instructions further cause the processing circuitry todetermine that the heart rate cycle has ended based on a second time theshort-term average of the heart rate of the patient changes from beingless than the long-term average of the heart rate of the patient tobeing greater than the long-term average of the heart rate of thepatient. The instructions further cause the processing circuitry todetermine a peak-to-valley time interval, the peak-to-valley timeinterval being a time interval between a maximum short-term average ofthe heart rate during the heart rate cycle and a minimum short-termaverage of the heart rate during the heart rate cycle. The instructionsfurther cause the processing circuitry to determine whether one or moreconditions of a plurality of conditions are satisfied for the heart ratecycle, where one condition of the plurality of conditions is that thepeak-to-valley time interval is greater than a lower peak-to-valley timethreshold and less than an upper peak-to-valley time threshold. Theinstructions further cause the processing circuitry to determine thatthe patient has experienced a sleep apnea episode based at least in parton one or more conditions being satisfied for the heart rate cycle. Theinstructions further cause the processing circuitry to generate anindication that the patient has experienced a sleep apnea episode.

The details of one or more examples of this disclosure are set forth inthe accompanying drawings and in the description below. Other features,objects, and advantages will be apparent from the description anddrawings, and from the claims.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a conceptual diagram illustrating an example medical devicesystem in conjunction with a patient, where the medical device system isconfigured to implement various sleep apnea detection and communicationfunctionalities of this disclosure.

FIG. 2 is a conceptual drawing illustrating another example medicaldevice system in conjunction with a patient, where the medical devicesystem is configured to detect a sleep apnea episode of the patientaccording to techniques of this disclosure.

FIG. 3 is a functional block diagram illustrating an exampleconfiguration of a medical device configured to sense and record cardiacelectrogram signals of a patient.

FIG. 4 is a line graph that illustrates sleep disordered heart rate(SDHR) information for a patient.

FIG. 5 is a flowchart illustrating a method by which a medical devicesystem that is configured according to aspects of this disclosure maydetect a sleep apnea episode.

DETAILED DESCRIPTION

In general, this disclosure is directed to detecting individual episodesof sleep apnea. Sleep apnea is a breathing disorder that cuts oxygensupply to various systems and organs of the body. To deal with thereduction in oxygenation levels, organs and systems of the body maytrigger one or more compensatory mechanisms. With respect to thecardiovascular system, the compensatory mechanism(s) cause the heart toincrease blood output for a period of time. As such, the cardiaccompensatory mechanisms cause increased exertion of the heart. Moreover,at the end of a sleep apnea episode and during a recovery period thatfollows a sleep apnea episode, the patient's heart rate may increasesignificantly, due to alveolar hyperventilation caused by the pulmonarysystem's compensatory mechanisms. The heart rate spike after a sleepapnea episode may be greater in magnitude naturally occurring heart rateincreases that are exhibited by the normal phenomenon of cyclicalvariation of heart rate (CVHR). As such, both the reduced oxygen supplyduring a sleep apnea episode and the hyperventilation that follows asleep apnea episode may cause exertion levels in the heart that exceednormal levels of heart exertion.

The abnormal oxygenation conditions associated with sleep apnea mayaffect various systems and vital organs adversely. Repeated instances ofincreased heart exertion, as may be caused by frequent compensatoryblood output to counter chronic sleep apnea and by increasing the heartrate to accommodate subsequent hyperventilation, increases thelikelihood of heart ailments or possible heart failure. Some existingmonitoring systems have relied on respiratory measurement data to detectan episode of sleep apnea. For example, algorithms based on cardiacsignals have been used to detect sleep apnea episodes. However, somesleep apnea algorithms may be inaccurate (e.g., have a significant falsepositive error rate), which may lead to inadequate treatment of sleepapnea.

Techniques described in this disclosure enable a medical monitoringsystem or therapy delivery system to leverage heart-related datacollections to accurately detect, and potentially counter, episodes ofsleep apnea. For instance, in one example, sensing circuitry of amedical monitoring system may sense a cardiac signal indicating activityof a heart of a patient. Additionally, processing circuitry of themedical monitoring system may determine, based on the cardiac signal, ashort-term average of a heart rate of the patient and a long-termaverage of the heart rate of the patient, the short-term average of theheart rate of the patient being based on fewer heart beats than thelong-term average of the heart rate of the patient.

The processing circuitry may determine that a heart rate cycle hasstarted based on a first time the short-term average of the heart rateof the patient changes from being less than the long-term average of theheart rate of the patient to being greater than the long-term average ofthe heart rate of the patient. Furthermore, the processing circuitry maydetermine that the heart rate cycle has ended based on a second time theshort-term average of the heart rate of the patient changes from beingless than the long-term average of the heart rate of the patient tobeing greater than the long-term average of the heart rate of thepatient.

Additionally, the processing circuitry may determine a series of heartrate variation values (HRV). For example, the processing circuitry maydetermine, based on heart beats detected during the heart rate cycle, apeak-to-valley heart rate variation value (e.g., the largest differencebetween a maximum short-term average of the heart rate during the heartrate cycle and a minimum short-term average of the heart rate during theheart rate cycle). That is, the peak-to-valley heart rate variationvalue may be the largest HRV value of the series of HRV values for theheart rate cycle. The processing circuitry may also determine apeak-to-valley time interval, the peak-to-valley time interval being atime interval between a maximum short-term average of the heart rateduring the heart rate cycle and a minimum short-term average of theheart rate during the heart rate cycle.

The processing circuitry may also determine whether one or moreconditions of a plurality of conditions are satisfied for one or moreheart rate cycles. The plurality of conditions may comprise apeak-to-valley time interval condition that the peak-to-valley timeinterval is greater than a lower peak-to-valley time threshold and lessthan an upper peak-to-valley time threshold. The processing circuitrymay determine that the patient has experienced a sleep apnea episodebased at least in part on one or more conditions being satisfied for oneor more heart rate cycles. Furthermore, the processing circuitry maygenerate an indication that the patient has experienced a sleep apneaepisode. Thus, by leveraging heart-related data collections to determinethe satisfaction of one or more condition of the plurality ofconditions, the medical monitoring system or therapy delivery system mayaccurately detect, and potentially counter, episodes of sleep apnea.

FIG. 1 is a conceptual diagram illustrating a medical device system 100Ain conjunction with a patient 102A. Medical device system 100A is anexample of medical device system 100 configured to implement thetechniques described herein for detecting a sleep apnea episode and forresponsively providing an indication indicating that the sleep apneaepisode is detected. In the example of FIG. 1 , medical device system100A may include a medical device 106A and an external device 108A.Medical device 106A may be an implantable medical device (IMD), such asan insertable cardiac monitor (ICM). In the example of FIG. 2 , amedical device system 100B includes a medical device 106B. Medicaldevice 106B may be another implantable medical device, such as animplantable pacemaker. This disclosure may refer to either of medicaldevice 106A or medical device 106B as “medical device 106.” In someexamples, medical device 106 is an external medical device.

Medical device 106A is capable of sensing and recording cardiacventricular electrogram (EGM) signals from a position outside of heart104A. In some examples, medical device 106A may include or be coupled toone or more additional sensors that generate one or more otherphysiological signals, such as signals that vary based on heart motionand/or sounds, blood pressure, blood flow, blood oxygenation, orrespiration. Medical device 106A may be implanted outside of the thoraxof patient, e.g., subcutaneously or submuscularly, such as the pectorallocation illustrated in FIG. 2 . In some examples, medical device 106Amay take the form of a Reveal LINQ™ ICM.

External device 108A may be a computing device, e.g., used in a home,ambulatory, clinic, or hospital setting, to communicate with medicaldevice 106A via wireless telemetry. External device 108A may be coupledto a remote patient monitoring system, such as Carelink®. Externaldevice 108A may be, for example, a programmer, external monitor, orconsumer device (e.g., smart phone), etc. External device 108A may beused to program commands or operating parameters into medical device106A for controlling its functioning, e.g., when configured as aprogrammer for medical device 106A. External device 108A may be used tointerrogate medical device 106A to retrieve data, including deviceoperational data as well as physiological data accumulated in memory.The interrogation may be automatic, e.g., according to a schedule, or inresponse to a remote or local user command. Programmers, externalmonitors, and consumer devices are examples of external devices 30A thatmay be used to interrogate medical device 106A. Examples ofcommunication techniques used by medical device 106A and external device108A may include tissue conductance communication (TCC), orradiofrequency (RF) telemetry, which may be an RF link established viaBluetooth®, WiFi®, or medical implant communication service (MICS).Medical device system 100A also may include implantable sensing device110, also referred to as sensor device 110.

External device 108A may wirelessly communicate with medical device106A, e.g., to program the functionality of the ICM, and to retrieverecorded physiological signals and/or patient parameter values or otherdata derived from such signals from the ICM. Both medical device 106Aand external device 108A may include processing circuitry, and theprocessing circuitry of either device, of both devices, or any otherdevice included in medical device system 100A may perform the techniquesdescribed herein, such as determining patient parameter values for aperiod and determining whether one or more patient parameter values isindicative of a sleep apnea episode.

Based on the analysis of patient's heart activity, the processingcircuitry of one or more of the devices may also be configured toprovide an indication to a user, e.g., to a clinician and/or to patient102A that a likely sleep apnea episode has been detected, e.g., viaexternal device 108A. For example, medical device system 100A may store(e.g., record) data associated with the occurrence of the likely sleepapnea episode in memory (e.g., as illustrated in FIG. 3 ) included inmedical device system 100A (e.g., memory of either medical device 106Aor external device 108A, or any other memory included in medical devicesystem 100A, such as memory included in a server, etc.). Medical devicesystem 100A may then automatically present (e.g., at a pre-determinedtime of day) the data (e.g., as part of a report or history of patient102A) in response to input from patient 102A or another person. Forexample, patient 102A or another person may press a button to causemedical device system 102A to present the data in a visual form (e.g.,display the data via a display, etc.), audio form (e.g., emit a sound,etc.), tactile form (e.g., vibration or vibrational pattern, etc.),and/or any other form suitable for communicating information to patient102A. Patient 102A, a clinician, or another implanted or externalmedical device may use the data to deliver or take a preventativemeasure to prevent the occurrence of additional sleep apnea episodes.

Medical device 106A may monitor physiological parameter value(s)indicative of patient state, such as heart rate, HRV values,peak-to-valley time intervals, activity count, peak-to-valley heart ratevariation values, cycle lengths, etc. Medical device 106A may measurethe physiological parameter value(s) all the time or at specific timesof the day and/or night. In some examples, sensor device 110 may be apart of sensor assembly 106A. Each of sensor device 110 and medicaldevice 106A may include a timer and processing circuitry configured todetermine a time of day based on the timer value. If sensor device 110determines that the current time is within a predetermined window thatmay be stored in memory of sensor device 110, sensor device 110 maymeasure and transmit the physiological parameter value(s) of patient tomedical device 106A.

In some examples, sensor device 110 may include wireless communicationcircuitry configured to receive a trigger signal from medical device106A. The pressure sensing circuitry of sensor device 110 may beconfigured to measure the physiological parameter value(s) of patient inresponse to receiving the trigger signal. In this manner, medical device106A may dictate the times at which sensor device 110 measuresphysiological parameter value(s), and sensor device 110 may enter alow-power mode such as sleep mode until the wireless communicationcircuitry of sensor device 110 receives a trigger signal. Medical device106A may transmit physiological parameter data (e.g., heart rate, HRVvalues, peak-to-valley time intervals, activity count, peak-to-valleyheart rate variation values, cycle lengths, etc.) acquired by medicaldevice 106A to external device 108A. Medical device 106A may alsotransmit physiological parameter value(s) measurements received fromsensor device 110 to external device 108A.

Although not illustrated in the example of FIG. 1 , a medical devicesystem configured to implement the techniques described in thisdisclosure may include one or more implanted or external medical devicesin addition to or instead of medical device 106A. For example, a medicaldevice system may include a vascular ICD, an extravascular ICD, acardiac pacemaker implanted outside of the heart 104A but coupled tointracardiac or epicardial leads, or an intracardiac pacing device. Oneor more such devices may generate signals, and include processingcircuitry configured to perform, in whole or in part, the techniquesdescribed herein for detecting sleep apnea episode(s). The implanteddevices may communicate with each other and/or an external device 108A,and one of the implanted or external devices may ultimately determinewhether sleep apnea is detected based on information received from theother device(s).

In various examples, implantable medical device (IMD) components may beconnected to leads that extend into heart 104A or could be implanted inheart 104A entirely. In some examples, components of medical devicesystem 100A may be external devices. Components of medical device system100A may be configured to detect cardiac electrogram signals, such as anECG. In various examples, processing circuitry of medical device system100A, such as processing circuitry of medical device 106A and/orexternal device 108A, may perform the techniques of this disclosureusing various types of sensing circuitry, such as sensing circuitry ofmedical device 106A and/or sensing circuitry of sensor device 110capable of detecting timing of cardiac depolarization or contraction. Assuch, aspects of medical device system 200A may detect sleep apneaepisodes using various types of measurements, such as cardiac cyclemetrics sensed by medical device 106A and/or pressure-based readingssensed by sensor device 110.

FIG. 2 is a conceptual drawing illustrating an example medical devicesystem 100B in conjunction with a patient 102B. Medical device system100A and medical device system 100B (either of which may be referred toas “medical device system 100”) are examples of medical device systemsconfigured to implement the techniques described herein for detecting asleep apnea episode based on whether one or more physiological parametervalues (e.g., peak-to-valley time interval, activity count,peak-to-valley heart rate variation value, cycle length, etc.) satisfyone or more conditions (e.g., the peak-to-valley time intervalcondition, the activity count condition, the peak-to-valley heart ratevariation condition, the cycle length condition, etc.). In someexamples, upon identifying a likely sleep apnea episode, components ofmedical device system 100B may also responsively provide an indicationindicating that a sleep apnea episode is likely occurring, and/ordeliver a therapy configured to prevent, mitigate, or remedy the effectsof the sleep apnea episode. In the illustrated example, medical devicesystem 100B includes medical device 106B coupled to a ventricular lead202 and an atrial lead 204.

Ventricular lead 202 and atrial lead 204 may be electrically coupled tomedical device 106B and extend into the patient's heart 104B.Ventricular lead 202 may include electrodes 206 and 208 shown positionedon the lead in the patient's right ventricle (RV) for sensing EGMsignals and pacing in the RV. Atrial lead 204 may include electrodes 210and 212 positioned on the lead in the patient's right atrium (RA) forsensing atrial EGM signals and pacing in the RA.

Medical device 106B may use both ventricular lead 202 and atrial lead204 to acquire cardiac electrogram (EGM) signals from heart 104B ofpatient 102B. Medical device system 100B is shown as having a dualchamber IMD configuration, but other examples may include one or moreadditional leads, such as a coronary sinus lead extending into the rightatrium, through the coronary sinus and into a cardiac vein to positionelectrodes along the left ventricle (LV) for sensing LV EGM signals anddelivering pacing pulses to the LV. In other examples, a medical devicesystem may be a single chamber system, or otherwise not include atriallead 204.

Processing circuitry, sensing circuitry, and other circuitry configuredfor performing the techniques described herein may be housed within asealed housing 200 of medical device 106B. Housing 200 (or a portionthereof) may be conductive to serve as an electrode for pacing orsensing. Medical device 106B may acquire signal data (e.g., EGM signaldata) and cardiac rhythm episode data and transmit the data to anexternal device 108B. External device 108B may be a computing device,e.g., a device used in a home, ambulatory, clinic, or hospital setting,comprising processing circuitry and/or communicative interfacingcircuitry configured to communicate with medical device 106B viawireless telemetry. External device 108B may be coupled to a remotepatient monitoring system, such as Carelink®. External device 108B mayinclude, be, or may be part of, in various examples, a programmer,external monitor, or consumer device, e.g., a smart phone.

External device 108B may be implemented and may operate in a mannersimilar to external device 108A. For example, external device 108B maybe used to program commands or operating parameters into medical device106B for controlling its functioning, e.g., when configured as aprogrammer for medical device 106B, and may be used to interrogatemedical device 106B to retrieve data, including device operational dataas well as physiological data accumulated in a memory of medical device106B. Medical device system 100B is an example of a medical devicesystem operable to leverage physiological parameter value(s) to detectsleep apnea episodes. For example, medical device system 100B may beconfigured to monitor physiological parameter value(s) and determinewhether one or more physiological parameter value satisfy one or moreconditions. In some examples, if medical device system 100B determinesthat a sleep apnea episode has likely occurred, medical device system100B may responsively provide an indication indicating that the event isdetected, and optionally, trigger delivery of a therapy configured toremediate the effects of the event or stem the progression of sucheffects. The techniques may be performed by processing circuitry ofmedical device system 100B, such as processing circuitry of one or bothof medical device 106B and external device 108B, individually, orcollectively.

The processing circuitry of external device 108B and/or medical device106B may determine the values of at least some patient parameters basedon signals generated by sensing circuitry of medical device 106B. Insome examples, medical device 106B may include or be coupled to one ormore other sensors that generate one or more other physiologicalsignals, such as signals that vary based on blood flow and/orrespiration. The processing circuitry of external device 108B and/ormedical device 106B may determine patient parameters based on therapydelivered by various components of medical device system 2B that areomitted from FIG. 2 for ease of illustration, such as a CPAP machine.For instance, the processing circuitry of external device 108B and/ormedical device 106B may analyze heart activity information to determinewhether a sleep apnea episode has been remediated effectively by way oftherapy delivered by the positive airway pressure machine or medicaldevice 106B.

FIG. 3 is a functional block diagram illustrating an exampleconfiguration of a medical device 106. Medical device 106 may correspondto medical device 106A in FIG. 1 , medical device 106B illustrated inFIG. 2 , or another medical device configured to implement thetechniques as described in this disclosure. Similarly, although notillustrated in FIG. 3 , external device 108 may correspond to externaldevice 108A in FIG. 1 , external device 108B illustrated in FIG. 2 , oranother external device configured to implement the techniques asdescribed in this disclosure.

In the illustrated example, medical device 106 includes processingcircuitry 302, memory 314, sensing circuitry 304, therapy deliverycircuitry 306, one or more sensors 308 (e.g., an accelerometer 310),communication circuitry 312, and timer 316. However, medical device 106does not need to include all these components in some examples, ormedical device 106 may include additional components in some examples.For example, medical device 106 may not include therapy deliverycircuitry 306 in some instances.

Memory 314 may include computer-readable instructions that, whenexecuted by processing circuitry, cause medical device 106 andprocessing circuitry to perform various functions attributed to medicaldevice 106 and processing circuitry herein (e.g., determining time ofday, comparing time of day to a predetermined window, causingcommunication circuitry 312 to transmit physiological parameter value(s)to an external device, etc.). Memory 314 may include any volatile,non-volatile, magnetic, optical, or electrical media, such as a randomaccess memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM),electrically-erasable programmable ROM (EEPROM), flash memory, or anyother digital or analog media. Memory 314 may store threshold(s) for thepeak-to-valley time interval condition, the activity count condition,the peak-to-valley heart rate variation condition, the cycle lengthcondition, etc. Memory 314 may also store data indicating physiologicalparameter value(s) measurements received from sensing device 110.

Processing circuitry 302 may include fixed function circuitry and/orprogrammable processing circuitry. Processing circuitry 302 may includeany one or more of a microprocessor, a controller, a digital signalprocessor (DSP), an application specific integrated circuit (ASIC), afield-programmable gate array (FPGA), or equivalent discrete or analoglogic circuitry. In some examples, processing circuitry 302 may includemultiple components, such as any combination of one or moremicroprocessors, one or more controllers, one or more DSPs, one or moreASICs, or one or more FPGAs, as well as other discrete or integratedlogic circuitry. The functions attributed to processing circuitry 302herein may be embodied as software, firmware, hardware or anycombination thereof. For example, processing circuitry 302 may beprocessing circuitry of either medical device 106 or external device 108or any other processing circuitry included in medical device system 100may be configured to perform techniques in accordance with thisdisclosure, such as determining patient parameter values for a periodand determining whether one or more patient parameter values areindicative of a sleep apnea episode.

Sensing circuitry 304 and therapy delivery circuitry 306 are coupled toelectrodes 300. Electrodes 300 illustrated in FIG. 3 may correspond to,for example, electrodes carried on leads 202 and/or 204 of medicaldevice system 200B (FIG. 2 ). Sensing circuitry 304 may monitor signalsfrom a selected two or more of electrodes 300 in order to monitorelectrical activity of heart, impedance, or other electrical phenomenon.Sensing of a cardiac electrical signal may be done to determine heartrates or HRV, or to detect arrhythmias (e.g., tachyarrhythmias orbradycardia) or other electrical signals. In some examples, sensingcircuitry 304 may include one or more filters and amplifiers forfiltering and amplifying a signal received from electrodes 300. In someexamples, sensing circuitry 304 may sense or detect physiologicalparameters, such as heart rate, blood pressure, respiration, and thelike.

The resulting cardiac electrical signal may be passed to cardiac eventdetection circuitry that detects a cardiac event when the cardiacelectrical signal crosses a sensing threshold. The cardiac eventdetection circuitry may include a rectifier, filter and/or amplifier, asense amplifier, comparator, and/or analog-to-digital converter.

Sensing circuitry 304 may also include a switch module to select whichof the available electrodes 300 (or electrode polarities) are used tosense the heart activity. In examples with several electrodes 300,processing circuitry 302 may select the electrodes that function assense electrodes, i.e., select the sensing configuration, via the switchmodule within sensing circuitry 304. Sensing circuitry 304 may also passone or more digitized EGM signals to processing circuitry 302 foranalysis, e.g., for use in cardiac activity discrimination (e.g.,cardiac rhythm discrimination).

In the example of FIG. 3 , medical device 106 includes one or moresensors 308 coupled to sensing circuitry 304. Although illustrated inFIG. 3 as being included within medical device 106, one or more ofsensors 308 may be external to medical device 106, e.g., coupled tomedical device 106 via one or more leads, or configured to wirelesslycommunicate with medical device 106. In some examples, sensors 308transduce a signal indicative of a patient parameter, which may beamplified, filtered, or otherwise processed by sensing circuitry 304. Insuch examples, processing circuitry 302 determines physiologicalparameter value(s) based on the signals. In some examples, sensors 308determine the physiological parameter value(s), and communicate them,e.g., via a wired or wireless connection, to processing circuitry.

In some examples, sensors 308 include one or more accelerometers 310,e.g., one or more three-axis accelerometers. Signals generated by theone or more accelerometers 310 may be indicative of, for example, heartsounds or other vibrations or movement associated with the beating ofthe heart, or coughing, rales, or other respiration abnormalities.Accelerometers 310 may produce and transmit signals to processingcircuitry 302 for a determination as to whether the heart 104 hascontracted. In some examples, sensors 308 may include one or moremicrophones configured to detect heart sounds or respirationabnormalities. In some examples, sensors 308 may include sensorsconfigured to transduce signals indicative of blood flow, oxygensaturation of blood, or patient temperature, and processing circuitrymay determine patient parameters values based on these signals.

Therapy delivery circuitry 306 is configured to generate and deliverelectrical therapy to the heart. Therapy delivery circuitry 306 mayinclude one or more pulse generators, capacitors, and/or othercomponents capable of generating and/or storing energy to deliver aspacing therapy, defibrillation therapy, cardioversion therapy, othertherapy or a combination of therapies. In some instances, therapydelivery circuitry 306 may include a first set of components configuredto provide pacing therapy and a second set of components configured toprovide anti-tachyarrhythmia shock therapy. In other instances, therapydelivery circuitry 306 may utilize the same set of components to provideboth pacing and anti-tachyarrhythmia shock therapy. In still otherinstances, therapy delivery circuitry 306 may share some of the pacingand shock therapy components while using other components solely forpacing or shock delivery.

Therapy delivery circuitry 306 may include charging circuitry, one ormore charge storage devices, such as one or more capacitors, andswitching circuitry that controls when the capacitor(s) are dischargedto electrodes 300 and the widths of pulses. Charging of capacitors to aprogrammed pulse amplitude and discharging of the capacitors for aprogrammed pulse width may be performed by therapy delivery circuitry306 according to control signals received from processing circuitry,which are provided by processing circuitry according to parametersstored in memory 314. Processing circuitry may control therapy deliverycircuitry 306 to deliver the generated therapy to the heart via one ormore combinations of electrodes 300, e.g., according to parametersstored in memory 314. Therapy delivery circuitry 306 may include switchcircuitry to select which of the available electrodes 300 are used todeliver the therapy, e.g., as controlled by processing circuitry.

Communication circuitry 312 may include any suitable hardware, firmware,software or any combination thereof for communicating with anotherdevice, such as an external device 108 or another IMD or sensor. Underthe control of processing circuitry, communication circuitry 312 mayreceive downlink telemetry from and send uplink telemetry to an externaldevice 108 or another device with the aid of an antenna, which may beinternal and/or external. In some examples, communication circuitry 312may communicate with a local external device, and processing circuitrymay communicate with a networked computing device via the local externaldevice and a computer network, such as the Medtronic CareLink® Network.

A clinician or other user may retrieve data from medical device 106using external device 108A or another local or networked computingdevice configured to communicate with processing circuitry viacommunication circuitry 312. The clinician may also program parametersof medical device 106 using external device 108 or another local ornetworked computing device.

Communication circuitry 312 may also be configured to communicate withan implantable pressure sensing device 110. Processing circuitry mayreceive measured physiological parameter value(s) values from sensingdevice 110 via communication circuitry 312. In some examples, processingcircuitry may send a trigger signal to sensing device 110 viacommunication circuitry 312 to control the sensing device to measurephysiological parameter value(s) in response to the trigger signal.

Although not illustrated in FIG. 3 , communication circuitry 312 may becoupled to electrodes 300 for tissue conductance communication (TCC) viathe electrodes. In some examples, communication with medical device 106and external device 108 may be via RF telemetry or TCC. In one example,communication circuitry 312 may be configured for RF telemetrycommunication with external device 108 and TCC with sensing device 110.

Medical device 106 and/or external device 108 may include timer 316.Timer 316 may be configured to provide a timer value, which processingcircuitry may use to measure and determine various physiologicalparameters that involve duration (e.g., HRV, cycle length, etc.).Additionally or alternatively, processing circuitry 302 may determine atime of day based on the timer value. For example, processing circuitry302 may be configured to determine a time of day based on the timervalue. Processing circuitry 302 may associate heart activity of thepatient with the time of day when the heart activity occurred. Forexample, processing circuitry may associate one or more sleep apneaepisodes with the time of day when the sleep apnea episodes occurred andstore this information in memory 314, which may be retrieved, forexample, during interrogation by external device 108.

FIG. 4 is a line graph 400 that illustrates sleep disordered heart rate(SDHR) information for patient. In FIG. 4 , the R-R interval correspondsto the interval between two successive R-waves. In the example of linegraph 400, the SDHR of patient exhibits, at various times,characteristics of different physiological conditions. For instance, theprocessing circuitry of medical device system 100A or 100B (collectively“medical device system 100”), such as processing circuitry of a medicaldevice 106 and/or external device 108, may analyze trends shown byclusters of maxima and minima (e.g., “peak” and “valley”) points of linegraph 400 to determine whether patient exhibits symptoms of a sleepapnea episode. Some peak-and-valley clusters that the processingcircuitry of medical device system 100 may analyze to detect possiblesleep apnea episodes with respect to a patient are identified in FIG. 4.

For example, even during normal breathing, the heart rate information ofline graph 400 illustrates a type of cyclical variable heart rate (CVHR)referred to as respiratory sinus arrhythmia (RSA). During RSA, the heartrate of the patient increases with an inspiration (or inhalation) andthe heart rate of patient decreases with expiration (or exhalation). RSAis associated with a single breath cycle, e.g., a single heart rateincrease with inspiration and a single heart rate decrease withexpiration. RSA is illustrated in the graph of FIG. 4 by higherfrequency and lower amplitude variations 408.

The lower frequency and higher amplitude variations of the graph of FIG.4 , on the other hand, illustrate a form of CVHR, referred to herein asSDHR, associated with sleep apnea. For example, each of areas 402 and404 illustrates one apnea arousal cycle, and the corresponding decreaseand increase in heart rate. In most patients, a decrease of heart rateis observed during each apnea event followed by an increase of heartrate near the end of the apnea event. The heart rate further increasesduring ensuing hyperventilation. In the example of the individual sleepapnea episode associated with area 404 of line graph 400, the heart rateincrease due to autonomic arousal is indicated by crest 406. Forinstance, crest 406 may indicate a decrease that heart 104 experiencesas the autonomic nervous system of patient attempts to compensate forthe increased oxygen saturation during recovery from a sleep apneaepisode. As such, troughs between such crests may be indicative of aperiod of overexertion of heart 104 which results from compensatorymechanisms that the autonomic nervous system of patient implements tocounter the decreased oxygen saturation caused by the sleep apneaepisode. As shown in FIG. 4 , near the end of the sleep apnea episode(e.g., after a valley 410), the heart rate may increase.

Again, the processing circuitry of medical device system 100 may collectand analyze physiological parameter value(s), determined in any way, toidentify sleep apnea events based on the characteristics illustrated bythe line graph 400 of FIG. 4 .

FIG. 5 is a flowchart illustrating a method by which a medical devicesystem 100 that is configured to perform techniques described in thisdisclosure may detect a sleep apnea episode. In accordance withtechniques of this disclosure, medical device system 100 may generate,by processing circuitry 302, a cardiac signal indicating activity of aheart of patient 102 in response to signals sensed by sensing circuitry304 of medical device 106. As discussed above, processing circuitry 302may be included in medical device 106, external device 108, or elsewherein medical device system 100. Medical device 106 may include one or moresensors 308 configured to sense signals produced by heart activity.Sensing circuitry 304 may deliver (e.g., send, transmit, etc.) detectedsignals to processing circuitry 302. Processing circuitry 302 may thengenerate (e.g., based on an algorithm) a cardiac signal indicating theactivity of a heart of patient 102. In some examples, the cardiac signalmay be associated with characteristics of the heart activity of patient102. For example, the cardiac signal may be associated with the waves,intervals, durations, and rhythm of the heart activity of patient 102.

Medical device system 100 may further determine, by processing circuitry302, a short-term average heart rate and a long-term average heart rateupon which the start and end of a heart rate cycle may be based (502).The short-term average of the heart rate of patient 102 may be based ona first number of heartbeats, and the long-term average of the heartrate of patient 102 may be based on a second number of heartbeats,wherein the first number is less than the second number. For example,the first number of heartbeats may be equal to 3 heartbeats and thesecond number of heartbeats may be equal to 120 heartbeats. In such anexample, the short-term average of the heart rate of patient 102 may bebased on 3 heartbeats, and the long-term average of the heart rate ofpatient 102 may be based on 120 heartbeats. In other examples, the firstnumber of heartbeats may be equal to a value other than 3 (e.g., 4, 5,etc.) and/or the second number of heartbeats may be equal to a valueother than 120 (e.g., 119, 130, etc.).

Medical device system 100 may further calculate, by processing circuitry302, the short-term average of the heart rate and the long-term averageof the heart rate may vary. For example, the short-term average of theheart rate and the long-term average of the heart rate may be the medianof the first number of heartbeats and the second number of heartbeats,respectively (e.g., the median of 3 heartbeats and the median of 120heartbeats, respectively). Alternatively, the short-term average of theheart rate and the long-term average of the heart rate may be the meanof the first number of heartbeats and the second number of heartbeats,respectively (e.g., the mean of 3 heartbeats and the mean of 120heartbeats, respectively). Alternatively, the short-term average of theheart rate and the long-term average of the heart rate may be the modeof the first number of heartbeats and the second number of heartbeats(e.g., the mode of 3 heartbeats and the mode of 120 heartbeats,respectively). It should be understood that other methods forcalculating the short-term average of the heart rate and the long-termaverage of the heart rate may be appropriate depending upon thecircumstances.

Processing circuitry 302 of medical device system 100 may furtherdetermine the start and end of a heart rate cycle (504). The heart ratecycle defines a period of time of the patient 102's heart activity thatis being processed by processing circuitry 302 to detect the occurrenceof a sleep apnea episode. The start of the heart rate cycle may be basedon a first time the short-term average of the heart rate of patient 102changes from being less than the long-term average of the heart rate ofpatient 102 to being greater than the long-term average of the heartrate of patient 102. For example, if the long-term average of the heartrate of patient 102 is a constant 65 beats per minute (BPM), then thestart of the heart rate cycle may be determined based on the first timethat the short-term average of the heart rate of patient 102 exceeds 65BPM. Similarly, the end of the heart rate cycle may be based on thesecond time the short-term average of the heart rate of patient 102changes from being less than long-term average of the heart rate ofpatient 102 to being greater than the long-term average of the heartrate of patient 102. For example, if the long-term average of the heartrate of patient 102 is a constant 65 BPM, then the end of the heart ratecycle may be determined based on the second time that the short-termaverage of the heart rate of patient 102 exceeds 65 BPM.

Processing circuitry 302 of medical device system 100 may furtherdetermine parameter values for patient 102 based on heart activityoccurring during the heart rate cycle (506). Determining whether patient102 has experienced a sleep apnea episode may be based on one or moreparameter values.

The parameter values to be determined by processing circuitry 302 mayinclude a peak-to-valley time interval. The peak-to-valley time intervalis the time interval between a maximum short-term average of the heartrate during the heart rate cycle and a minimum short-term average of theheart rate during the heart rate cycle. For example, if a maximumshort-term average of the heart rate value of 75 BPM occurs 30 secondsfrom a reference point (e.g., the beginning of a recording of the heartrate cycle, the beginning of the heart rate cycle, etc.) and during theheart rate cycle and the minimum short-term heart rate average value of60 BPM occurs 45 seconds from the reference point and during the heartrate cycle, then the peak-to-valley time interval is equal to 15seconds.

The parameter values to be determined by processing circuitry 302 mayfurther include an activity count. The activity count for the heart ratecycle indicates a number of time intervals during the heart rate cyclein which an amount of movement of the patient 102 is greater than aminimum movement threshold. Amount of movement of patient 102 may bedetermined using one or more sensors 308 (e.g., accelerometer 310). Forexample, accelerometer 310 may measure the acceleration of patient'sbody, and if the acceleration of patient's body exceeds an accelerationthreshold corresponding to a minimum movement threshold, the activitycount for the heart rate cycle may be incremented by one. As such, if,in a single heart rate cycle, the acceleration of patient's body exceedsthe acceleration threshold 8 times, the minimum movement threshold mayalso be exceeded 8 times so that the activity count for patient 102 is8.

The parameter values to be determined by processing circuitry mayfurther include a peak-to-valley heart rate variation value. Thepeak-to-valley heart rate variation value for the heart rate cycle isthe difference between a maximum short-term average of the heart rateduring the heart rate cycle and a minimum short-term average of theheart rate during the heart rate cycle. For example, if the maximumshort-term average of the heart rate during the heart rate cycle is 75BPM and the minimum short-term average of the heart rate during theheart rate cycle is 60 BPM, then the peak-to-valley heart rate variationvalue is 15 BPM for this heart rate cycle.

The parameter values to be determined by processing circuitry 302 mayfurther include a cycle length, where the cycle length for the heartrate cycle indicates a length of the heart rate cycle. For example, ifthe start of the heart rate cycle is at first time of 0 seconds (e.g.,based on the first time that the short-term average of the heart rate ofpatient 102 exceeds the long-term average of the heart rate of patient102), and the end of the heart rate cycle is at second time of 70seconds (e.g., based on the second time that the short-term average ofthe heart rate of patient 102 exceeds the long-term average of the heartrate of patient 102), the cycle length is 70 seconds.

In some examples, processing circuitry 302 may use a parameter value todetermine another parameter value. For example, processing circuitry 302may use a series of heart rate values to determine the peak-to-valleytime interval, which in turn may be used to determine whether patient102 experienced a sleep apnea episode. For example, if the short-termaverage of the heart rate of patient 102 fluctuates from 70 BPM to 60BPM, 60 BPM to 85 BPM, 85 BPM to 65 BPM, and so on during the heartcycle, heart rate values may include 10 BPM (70 BPM less 60 BPM), 25 BPM(85 BPM less 60 BPM), 5 BPM (70 BPM less 65 BPM), and so on. In thisexample, the peak-to-valley variation value is 25 BPM because 85 BPM isthe maximum short-term heart rate average value during the heart ratecycle, and 60 BPM is the minimum short-term average value during theheart rate cycle.

Processing circuitry 302 may then use the respective times the maximumshort-term average of the heart rate value of 85 BPM and the minimumshort-term average of the heart rate value of 60 BPM occurred todetermine the peak-to-valley time interval. For example, if the maximumshort-term average of the heart rate value of 85 BPM occurred 30 secondsfrom the start of the heart rate cycle and the minimum short-termaverage of the heart rate value of 60 BPM occurred 60 seconds from thestart of the heart rate cycle, then the peak-to-valley time interval isequal to 30 seconds.

Processing circuitry 302 of medical device system 100 may furtherdetermine whether one or more conditions of a plurality of conditionsare satisfied by one or more parameter values for patient 102 for theheart rate cycle. The plurality of conditions may include, but is notlimited to, a peak-to-valley time interval condition, an activity countcondition, a peak-to-valley heart rate variation condition, a cyclelength condition, etc.

The plurality of conditions may include a peak-to-valley time intervalcondition. The peak-to-valley time interval condition may be a conditionthat the peak-to-valley time interval is greater than a lowerpeak-to-valley time threshold and less than an upper peak-to-valley timethreshold. For example, if the lower peak-to-valley time threshold isequal to 5 seconds and the upper peak-to-valley time threshold is equalto 30 seconds, then processing circuitry 302 may determine that thepeak-to-valley time interval condition is satisfied if thepeak-to-valley time interval is greater than 5 seconds and less than 30seconds. As such, a peak-to-valley time interval of 10 seconds, forexample, would satisfy the peak-to-valley time interval condition.Alternatively, processing circuitry 302 may determine that thepeak-to-valley time interval condition is not satisfied if thepeak-to-valley time interval is not greater than 5 seconds and less than30 seconds. As such, a peak-to-valley time interval of 3 seconds, forexample, would not satisfy the peak-to-valley time interval condition.

Additionally or alternatively, the plurality of conditions may includean activity count condition. The activity count condition may be acondition that an activity count for the heart rate cycle is less thanan activity count threshold. For example, if the activity countthreshold is equal to 8, then processing circuitry 302 may determinethat the activity count condition is satisfied if the activity count isless than 8. As such, an activity count of 5, for example, would satisfythe activity count condition. Alternatively, processing circuitry 302may determine that the activity count condition is not satisfied if theactivity count is not less than 8. As such, an activity count of 10, forexample, would not satisfy the activity count condition.

Additionally or alternatively, the plurality of conditions may include apeak-to-valley HRV condition. The peak-to-valley HRV condition may be acondition that the peak-to-valley HRV value for the heart rate cycle isgreater than a lower peak-to-valley heart rate variation threshold andless than an upper peak-to-valley heart rate variation threshold. Forexample, if the lower peak-to-valley heart rate variation threshold isequal to 6 BPM and the upper peak-to-valley heart rate variationthreshold is equal to 50 BPM, then processing circuitry 302 maydetermine that the peak-to-valley HRV condition is satisfied if thepeak-to-valley HRV value is greater than 6 and less than 50. As such, apeak-to-valley HRV value of 25, for example, would satisfy thepeak-to-valley HRV condition. Alternatively, processing circuitry 302may determine that the peak-to-valley HRV condition is not satisfied ifthe peak-to-valley HRV value is not less than 50. As such, apeak-to-valley HRV value of 60, for example, would not satisfy thepeak-to-valley HRV condition.

Additionally or alternatively, the plurality of conditions may include acycle length condition. The cycle length is the length of the heart ratecycle (e.g., the length of the period of time of patient 102's heartactivity being processed by processing circuitry 302 for detecting asleep apnea episode). The cycle length condition may be a condition thatthe cycle length for the heart rate cycle is greater than a lower cyclelength threshold and less than an upper cycle length threshold. Forexample, if the lower cycle length threshold is equal to 25 seconds andthe upper cycle length threshold is equal to 100 seconds, thenprocessing circuitry 302 may determine that the cycle length conditionis satisfied if the cycle length is greater than 25 seconds and lessthan 100 seconds. As such, a cycle length of 50 seconds, for example,would satisfy the cycle length condition. Alternatively, processingcircuitry 302 determine that the cycle length condition is not satisfiedif the cycle length is not greater than 25 seconds and less than 100seconds. As such, a cycle length of 125 seconds, for example, would notsatisfy the cycle length condition.

In some examples, the plurality of conditions may include thepeak-to-valley time interval condition, the activity count condition,the peak-to-valley heart rate variation condition, and the cycle lengthcondition. In such examples, determining that the patient hasexperienced the sleep apnea episode may be based at least in part oneach of the conditions of the plurality of conditions being satisfiedfor the heart rate cycle.

In some examples, medical device system 100 may further determinewhether a patient 102 has experienced a sleep apnea episode based onwhether one or more conditions of the plurality of conditions aresatisfied (508). For example, if each of the one or more conditions issatisfied (e.g., peak-to-valley time interval condition is satisfied)(“YES” branch of 508), processing circuitry 302 may determine thatpatient 102 has experienced a sleep apnea episode or that theprobability that patient 102 has experienced a sleep apnea episode isnot low (510). Processing circuitry 302 may then cause the generation ofan indication that patient 102 has experienced a sleep apnea event(512). For example, processing circuitry 302 may cause the medicaldevice and/or an external device to generate an indication that patient102 has experienced a sleep apnea episode.

Alternatively, if one or more of the conditions have not been satisfied(“NO” branch of 508), processing circuitry 302 may determine thatpatient 102 has not experienced a sleep apnea episode or that theprobability that patient 102 has experienced a sleep apnea episode islow (514). Processing circuitry 302 may then cause the generation of anindication that patient 102 has not experienced a sleep apnea event(516). For example, processing circuitry 302 may cause the medicaldevice and/or an external device to generate an indication that patient102 has not experienced a sleep apnea episode. Alternatively, in someexamples, processing circuitry 302 does not generate an indication thatpatient 102 has experienced a sleep apnea event if patient 102 has notexperienced a sleep apnea event.

Processing circuitry 302 may generate an indication that patient 102 hasexperienced a sleep apnea episode. For example, processing circuitry 302may cause the medical device or an external device to output anindication that patient 102 has experienced a sleep apnea episode. Theexternal device may include one or more cellular phones, a ‘smartphone,’a satellite phone, a notebook computer, a tablet computer, a wearabledevice, a computer workstation, a personal digital assistant, a handheldcomputing device, a virtual reality headset, or any other device thatmay output an indication that patient 102 has experienced a sleep apneaepisode. External device may output the indication automatically (e.g.,at a pre-determined time of day) and/or in response to input frompatient 102 as part of a report or history of patient and in the form ofan audible notification, a visual notification, a tactile notification(e.g., a vibration or vibrational pattern), a text prompt, a buttonprompt, and/or any other notification that may indicate to patient 102that patient 102 has experienced a sleep apnea episode.

Although not illustrated in FIG. 5 , processing circuitry 302 maydetermine a confidence value, such as a probability, that patient 102has experienced a sleep apnea episode based on the number of conditionsof the plurality of conditions that are satisfied. For example, if onlyone condition is satisfied, processing circuitry 302 may determine thatthe confidence value that patient 102 has experienced a sleep apneaepisode is low. In another example, if two conditions are satisfied,processing circuitry 302 may determine that the confidence value thatpatient 102 has experienced a sleep apnea episode is moderate. Inanother example, if three or more conditions are satisfied, processingcircuitry 302 may determine that the confidence value that patient 102has experienced a sleep apnea episode is high.

Although not illustrated in FIG. 5 , medical device system 100 maydetermine, by processing circuitry 302, that patient 102 has experienceda sleep apnea episode based on one or more conditions being satisfied byat least a non-zero minimum number of additional heart rate cycles otherthan the current heart rate cycle that occur within a predeterminedtemporal distance (e.g., amount of time) of the current heart ratecycle. For example, if the minimum number of additional heart ratecycles is equal to 2 and the predetermined temporal distance is equal to240 seconds, then processing circuitry 302 may determine that patient102 has experienced a sleep apnea episode based on one or moreconditions (e.g., the peak-to-valley time interval condition, theactivity count condition, the peak-to-valley heart rate variationcondition, the cycle length condition, etc.) being satisfied by 2additional heart rate cycles other than the current heart rate cyclethat occur within 240 seconds of the current heart rate cycle. As such,processing circuitry 302 may determine that patient 102 has experienceda sleep apnea episode based on the activity count condition beingsatisfied by a heart rate cycle with a temporal distance of 220 secondsof the current heart rate cycle and the peak-to-valley heart ratevariation condition being satisfied by a heart rate cycle with atemporal distance of 75 seconds of the current heart rate cycle.Alternatively, processing circuitry 302 may determine that patient 102has not experienced a sleep apnea episode based on the activity countcondition being satisfied by a heart rate cycle with a temporal distanceof 300 seconds of the current heart rate cycle and the peak-to-valleyheart rate variation condition being satisfied by a heart rate cyclewith a temporal distance of 75 seconds of the current heart rate cyclebecause the heart rate cycle with a temporal distance of 300 seconds didnot occur within the predetermined temporal distance of 240 seconds.Additionally or alternatively, in the same example, processing circuitry302 may determine that patient 102 has not experienced a sleep apneaepisode because one or more conditions were not satisfied by 2 or moreadditional heart rate cycles (e.g., one condition was only satisfied by1 additional heart rate cycle because only the activity count conditionwas satisfied by only 1 heart rate cycle with a temporal distance of 75seconds of the current heart rate cycle).

The reference point for determining the temporal distance may vary. Forexample, the reference point for determining the temporal distance maybe the start of a heart rate cycle, the end of the heart rate cycle, andany point in between the start and end of the heart rate cycle. In someexamples, the direction of the temporal distance may vary. That is, thedirection of the temporal distance may be retrospective so that heartactivity that already occurred may be evaluated, prospective so thatheart activity that will occur may be evaluated, or a combination ofboth.

For example, if the reference point for determining the temporaldistance is the start of the heart rate cycle and the predeterminedtemporal distance is 240 seconds, then processing circuitry 302 maydetermine that patient 102 has experienced a sleep apnea episode basedon heart activity that occurred within any heart rate cycle before thestart of the current heart rate cycle with a temporal distance of 240seconds or less. In another example, processing circuitry 302 maydetermine that patient 102 has experienced a sleep apnea episode basedon heart activity that occurred within any heart rate cycle after thestart of the current heart rate cycle with a temporal distance of 240seconds or less. In yet another example, processing circuitry 302 maydetermine that patient 102 has experienced a sleep apnea episode basedon heart activity occurring within any heart rate cycle before the startof the current heart rate cycle and after the start of the current heartcycle, as long as the heart activity satisfying the one or moreconditions occur within a temporal distance of 240 seconds or less.

In another example, if the reference point for determining the temporaldistance is the end of the heart rate cycle and the predeterminedtemporal distance is 240 seconds, then processing circuitry 302 maydetermine that patient 102 has experienced a sleep apnea episode basedon heart activity that occurred within any heart rate cycle before theend of the current heart rate cycle with a temporal distance of 240seconds or less. Alternatively, processing circuitry 302 may determinethat patient 102 has experienced a sleep apnea episode based on heartactivity that occurred within any heart rate cycle after the end of thecurrent heart rate cycle with a temporal distance of 240 seconds orless. Alternatively, processing circuitry 302 may determine that patient102 has experienced a sleep apnea episode based on heart activity thatoccurred within any heart rate cycle before the end of the current heartrate cycle with a temporal distance of 120 seconds or less and withinany heart rate cycle after the end of the current heart rate cycle witha temporal distance of 120 seconds or less.

In another example, if the reference point for determining the temporaldistance is the midpoint of the heart rate cycle and the predeterminedtemporal distance is 240 seconds, then processing circuitry 302 maydetermine that patient 102 has experienced a sleep apnea episode basedon heart activity that occurred within any heart rate cycle before themidpoint of the current heart rate cycle with a temporal distance of 240seconds or less. Alternatively, processing circuitry 302 may determinethat patient 102 has experienced a sleep apnea episode based on heartactivity that occurred within any heart rate cycle after the midpoint ofthe current heart rate cycle with a temporal distance of 240 seconds orless. Alternatively, processing circuitry 302 may determine that patient102 has experienced a sleep apnea episode based on heart activity thatoccurred within any heart rate cycle before the midpoint of the currentheart rate cycle with a temporal distance of 120 seconds or less andwithin any heart rate cycle after the midpoint of the current heart ratecycle with a temporal distance of 120 seconds or less.

As used herein, an implantable medical device (IMD) may include, be, orbe part of a variety of devices or integrated systems, such as, but notlimited to, implantable cardiac monitors (ICMs), implantable pacemakers,including those that deliver cardiac resynchronization therapy (CRT),implantable cardioverter defibrillators (ICDs), diagnostics device,cardiac device, etc. Various examples have been described that includedetecting episodes of sleep apnea using cardiac cycle length metrics. Inaddition, pulmonary therapy may be provided to mitigate the severity ofthe sleep apnea episode or counter the effects of the sleep apneaepisode. Any combination of detection and therapy for sleep apneaepisodes is contemplated.

Various aspects of the techniques may be implemented within one or moreprocessing circuitries, including one or more microprocessors, DSPs,ASICs, FPGAs, or any other equivalent integrated or discrete logiccircuitry, as well as any combinations of such components, embodied inexternal devices, such as physician or patient external devices,electrical stimulators, or other devices. The term “processingcircuitry” may generally refer to any of the foregoing logic circuitry,alone or in combination with other logic circuitry or any otherequivalent circuitry.

In one or more examples, the functions described in this disclosure maybe implemented in hardware, software, firmware, or any combinationthereof. If implemented in software, the functions may be stored on, asone or more instructions or code, a computer-readable medium andexecuted by a hardware-based processing unit. Computer-readable mediamay include computer-readable storage media forming a tangible,non-transitory medium. Instructions may be executed by one or moreprocessing circuitries, such as one or more DSPs, ASICs, FPGAs, generalpurpose microprocessors, or other equivalent integrated or discretelogic circuitry. Accordingly, the term “processing circuitry,” as usedherein may refer to one or more of any of the foregoing structure or anyother structure suitable for implementation of the techniques describedherein.

In addition, in some aspects, the functionality described herein may beprovided within dedicated hardware and/or software modules. Depiction ofdifferent features as modules or units is intended to highlightdifferent functional aspects and does not necessarily imply that suchmodules or units must be realized by separate hardware or softwarecomponents. Rather, functionality associated with one or more modules orunits may be performed by separate hardware or software components, orintegrated within common or separate hardware or software components.Also, the techniques could be fully implemented in one or more circuitsor logic elements. The techniques of this disclosure may be implementedin a wide variety of devices or apparatuses, including an IMD, anexternal device, a combination of an IMD and external device, anintegrated circuit (IC) or a set of ICs, and/or discrete electricalcircuitry, residing in an IMD and/or external device.

What is claimed is:
 1. A system comprising: an implantable medicaldevice comprising: a set of electrodes; and sensing circuitry configuredto sense, via the set of electrodes, a cardiac signal indicatingactivity of a heart of a patient; and processing circuitry configuredto: determine a heart rate cycle based on a short-term average of aheart rate and a long-term average of the heart rate, wherein the heartrate cycle defines a period of time of the activity of the heart fordetecting an occurrence of a sleep apnea episode, and wherein theshort-term average and the long-term average are based on the cardiacsignal; determine whether a peak-to-valley time interval of the heartrate cycle satisfies a peak-to-valley time interval condition, whereinthe peak-to-valley time interval is a time interval between a maximumshort-term average of the heart rate during the heart rate cycle and aminimum short-term average of the heart rate during the heart ratecycle; and based at least in part on determining that the peak-to-valleytime interval satisfies the peak-to-valley time interval condition,output an indication that the sleep apnea episode occurred during theheart rate cycle.
 2. The system of claim 1, wherein the processingcircuitry is configured to determine that the peak-to-valley timeinterval satisfies the peak-to-valley time interval condition inresponse to the peak-to-valley time interval being greater than a lowerpeak-to-valley time threshold and less than an upper peak-to-valley timethreshold.
 3. The system of claim 1, wherein the processing circuitry isfurther configured to: determine whether an activity count of the heartrate cycle satisfies an activity count condition, wherein the activitycount indicates a number of time intervals during the heart rate cyclein which an amount of movement of the patient is greater than a minimummovement threshold; and based at least in part on determining that theactivity count satisfies the activity count condition and determiningthat the peak-to-valley time interval satisfies the peak-to-valley timeinterval, output the indication that the sleep apnea episode occurredduring the heart rate cycle.
 4. The system of claim 3, wherein theprocessing circuitry is configured to determine that the activity countsatisfies the activity count condition in response to the activity countbeing less than an activity count threshold.
 5. The system of claim 4,wherein the activity count threshold is equal to
 8. 6. The system ofclaim 1, wherein the processing circuitry is further configured to:determine whether a peak-to-valley heart rate variation value of theheart rate cycle satisfies a peak-to-valley heart rate variationcondition, wherein the peak-to-valley heart rate variation valueindicates a difference between the maximum short-term average of theheart rate during the heart rate cycle and the minimum short-termaverage of the heart rate during the heart rate cycle; and based atleast in part on determining that the peak-to-valley heart ratevariation value satisfies the peak-to-valley heart rate variationcondition and determining that the peak-to-valley time intervalsatisfies the peak-to-valley time interval, output the indication thatthe sleep apnea episode occurred during the heart rate cycle.
 7. Thesystem of claim 6, wherein the processing circuitry is configured todetermine that the peak-to-valley heart rate variation value satisfiesthe peak-to-valley heart rate variation condition in response to thepeak-to-valley heart rate variation value being between a lowerpeak-to-valley heart rate variation threshold and an upperpeak-to-valley heart rate variation threshold.
 8. The system of claim 7,wherein at least one of the lower peak-to-valley heart rate variationthreshold is equal to 6 beats per minute, or the upper peak-to-valleyheart rate variation threshold is equal to 50 beats per minute.
 9. Thesystem of claim 1, wherein the processing circuitry is furtherconfigured to: determine whether a cycle length of the heart rate cyclesatisfies a cycle length condition, wherein the cycle length indicates alength of the heart rate cycle; and based at least in part ondetermining that the cycle length satisfies the cycle length conditionand determining that the peak-to-valley time interval satisfies thepeak-to-valley time interval, output the indication that the sleep apneaepisode occurred during the heart rate cycle.
 10. The system of claim 9,wherein the processing circuitry is configured to determine that thecycle length satisfies the cycle length condition in response to thecycle length being greater than a lower cycle length threshold and lessthan an upper cycle length threshold.
 11. The system of claim 1, whereinthe heart rate cycle is a current heart rate cycle, and wherein theprocessing circuitry is further configured to: determine whether atleast one additional heart rate cycle that occurs within a predeterminedtemporal distance of the current heart rate cycle satisfies at least onecondition of a plurality of conditions, wherein each condition of theplurality of conditions is associated with the occurrence of the sleepapnea episode; and based at least in part on determining that the atleast one additional heart rate cycle satisfies the at least onecondition of the plurality of conditions, output an indication that thesleep apnea episode occurred during the current heart rate cycle. 12.The system of claim 11, wherein the plurality of conditions comprises atleast one of the peak-to-valley time interval condition, an activitycount condition, a peak-to-valley heart rate variation condition, or acycle length condition.
 13. The system of claim 12, wherein thepredetermined temporal distance is 240 seconds.
 14. The system of claim1, wherein the implantable medical device comprises the processingcircuitry, and wherein the system further comprises an external deviceconfigured to receive the indication that the sleep apnea episodeoccurred during the heart rate cycle.
 15. The system of claim 1,wherein, to determine the heart rate cycle, the processing circuitry isconfigured to: determine a start of the heart rate cycle based on afirst time the short-term average of the heart rate changes from beingless than the long-term average of the heart rate to being greater thanthe long-term average of the heart rate; and determine an end of theheart rate cycle based on a second time the short-term average of theheart rate changes from being less than the long-term average of theheart rate to being greater than the long-term average of the heart rateof the patient.
 16. An implantable medical device comprising: a set ofelectrodes; sensing circuitry configured to sense, via the set ofelectrodes, a cardiac signal indicating activity of a heart of apatient; and processing circuitry configured to: determine a heart ratecycle based on a short-term average of a heart rate and a long-termaverage of the heart rate, wherein the heart rate cycle defines a periodof time of the activity of the heart for detecting an occurrence of asleep apnea episode, and wherein the short-term average and thelong-term average are based on the cardiac signal; determine whether apeak-to-valley time interval of the heart rate cycle satisfies apeak-to-valley time interval condition, wherein the peak-to-valley timeinterval is a time interval between a maximum short-term average of theheart rate during the heart rate cycle and a minimum short-term averageof the heart rate during the heart rate cycle; and based at least inpart on determining that the peak-to-valley time interval satisfies thepeak-to-valley time interval condition, output an indication that thesleep apnea episode occurred during the heart rate cycle.
 17. Theimplantable medical device of claim 16, wherein the processing circuitryis configured to determine that the peak-to-valley time intervalsatisfies the peak-to-valley time interval condition in response to thepeak-to-valley time interval being greater than a lower peak-to-valleytime threshold and less than an upper peak-to-valley time threshold. 18.The implantable medical device of claim 17, wherein the processingcircuitry is further configured to: determine whether an activity countof the heart rate cycle satisfies an activity count condition, whereinthe activity count indicates a number of time intervals during the heartrate cycle in which an amount of movement of the patient is greater thana minimum movement threshold; and based at least in part on determiningthat the activity count satisfies the activity count condition anddetermining that the peak-to-valley time interval satisfies thepeak-to-valley time interval, output the indication that the sleep apneaepisode occurred during the heart rate cycle.
 19. A method comprising:determining, by processing circuitry, a heart rate cycle based on ashort-term average of a heart rate and a long-term average of the heartrate, wherein the heart rate cycle defines a period of time of theactivity of a heart of a patient for detecting an occurrence of a sleepapnea episode, and wherein the short-term average and the long-termaverage are based on the cardiac signal; determining, by the processingcircuitry, whether a peak-to-valley time interval of the heart ratecycle satisfies a peak-to-valley time interval condition, wherein thepeak-to-valley time interval is a time interval between a maximumshort-term average of the heart rate during the heart rate cycle and aminimum short-term average of the heart rate during the heart ratecycle; and based at least in part on determining that the peak-to-valleytime interval satisfies the peak-to-valley time interval condition,outputting, by the processing circuitry, an indication that the sleepapnea episode occurred during the heart rate cycle.
 20. The method ofclaim 1, further comprising determining, by the processing circuitry,that the peak-to-valley time interval satisfies the peak-to-valley timeinterval condition in response to the peak-to-valley time interval beinggreater than a lower peak-to-valley time threshold and less than anupper peak-to-valley time threshold.